This disclosure relates to compression of hyperspectral image data, based on an optimized set of basis vectors. It may be understood that compression reduces the size of a data set, but typically result in a loss of access to information content. In some cases, certain data from the data set is irretrievably lost (i.e. lossy compression), while in other cases the entirety of the data is maintained (i.e. lossless compression). Although in some cases information from the data set may be readily accessed and utilized while the data set is in its reduced size (i.e. dimensionality reduction), it is generally understood that other compression techniques would require an additional decompression step in order to return the data to a usable form.
Hyperspectral sensors can collect image data across a multitude of spectral bands through a combination of technology associated with spectroscopy and remote imaging. Thus, such sensors can capture sufficient information to derive an approximation of the spectrum for each pixel in an image. In addition to having a color value, and intensity, each pixel in the image additionally has a third dimension for a vector providing distinct information for the pixel over a large spectrum of wavelengths. This contiguous spectrum may be analyzed to separate and evaluate differing wavelengths, which may permit finer resolution and greater perception of information contained in the image. From such data, hyperspectral imaging systems may be able to characterize targets, materials, and changes to an image, providing a detection granularity which may exceed the actual resolution of pixels in the image and a change identification capability that does not require pixel level registration, which may provide benefits in a wide array of practical applications.
Because each pixel carries information over a wide spectrum of wavelengths, the size of a hyperspectral data set may often quickly become unwieldy in terms of the size of data that is being recorded by the hyperspectral sensor. As an example, hyperspectral sensors are often located remotely on satellites or aircraft capable of imaging areas in excess of 500 km×500 km per hour, which may result in the hyperspectral sensors generating anywhere from three to fifteen gigabits of data per second. Where the hyperspectral data needs to be processed in near real time, the large size of the data may introduce latency problems. In some cases, it may be desirable to transmit the data to a remote location for processing or other analysis, which again would make a reduced data size desirable. It may be appreciated that in some sensor platforms (such as those mounted on a drone or other mobile platform), direct data retrieval may be delayed by significant timescales as the mobile platform is gathering more data. As such, on-board processing and data reduction for advance transmission may facilitate preliminary or urgent data analysis.
Although the transmission rate for hyperspectral images can be increased using existing lossy and/or lossless compression techniques, these techniques also suffer from various drawbacks. For example, while lossy compression methods may be fine for casual photographs or other human viewable images, wherein the data that is removed may be beyond the eye's ability to resolve, applying such lossy compression methods to a hyperspectral data set may remove information that is valuable and desired for further computer or mathematical processing. Such removal of data may undermine the ability to characterize targets, materials, or changes to scenes that are captured in hyperspectral images. Lossless data compression would not remove such valuable information, since lossless algorithms produce a new data set that can subsequently be decompressed to extract the original data set. Although general purpose lossless compression algorithms can theoretically be used on any type of data, existing lossless compression algorithms typically cannot achieve significant compression on a different type of data than that which the algorithms were designed to compress. Thus, existing lossless compression algorithms do not provide a suitable guaranteed compression factor for hyperspectral images, and in certain cases, the decompressed data set may even be larger than the original data set.
It may be appreciated that a greater reduction of data size may be realized by a compression technique than by a dimensionality reduction technique. In some cases, the greater reduction in data size may outweigh the benefit of being able to process dimensionally reduced data. It may be appreciated, however, that once such data is compressed, an additional decompression step would generally be required in order to process the data. Although some compression techniques may be applied to hyperspectral image data directly, in some cases the hyperspectral image data may be dimensionally reduced prior to being further reduced in size through compression. It may be appreciated that such compression may allow for a greater reduction in data size, while still permitting faster processing of the most relevant data once it is decompressed. In the context of hyperspectral imaging, such processing generally means that the data is exploited for target detection, anomaly detection, material identification, classification mapping, or so on. In some cases, the dimensionally reduced hyperspectral data that may be further compressed may include a family of functions or a set of vectors whose arithmetic combination can represent all of the data in a three-dimensional (3D) data set. Hyperspectral image data is generally discrete, so at each X/Y location in a hyperspectral image the spectral data may form elements of a vector. Depending on the nature of these vectors, they may either be characterized as endmembers or basis vectors. While basis vectors span the data obtained from the image, and form a mathematical basis for the data, endmembers are pixels from an imaged scene (or extrapolations of pixels in the scene), that represent the spectra of a pure material found in the scene. In some cases, endmembers are derived such that they enclose or bound the data set (as in a hypervolume or a simplex).
Because dimensionally reduced data (DIMRED data) may be generated from hyperspectral image data (HSI data) processed using one or more of a variety of analysis techniques, it may be understood that compression of such DIMRED data may be performed as a post-processing technique subsequent to the dimensionality reduction process. As an example, compression post-processing techniques may be applied to the dimensionality reduced output of techniques such as those disclosed in the related applications incorporated by reference above, which compute geometric basis vectors. As another example, compression may also be applied to the dimensionality reduced outputs of other hyperspectral image processing mechanisms, including but not limited to Principal Components Analysis, which computes “statistically derived’ basis vectors that span a scene in an optimal mean-square sense. Regardless, it may be appreciated that among other things, it is advantageous to increase the speed at which the dimensionality of hyperspectral images is reduced, improve reduction of data volume sizes, improve the spectral fidelity of hyperspectral image data to reduce large volumes of sensor data and/or identify and preserve unusual pixels and/or subtle spectral variations from compression.